Data insights gained from the healthcare network can guide training and skills development.
Despite considerable progress in providing adequate healthcare to African citizens, the World Bank reports that 80 percent of Africans rely on public health facilities that often suffer from chronic shortages of medicines and skills. The World Health Organisation reports that half of all children under five who succumb to pneumonia, measles, tuberculosis and malaria live in Africa.
This means patients, doctors, specialists, researchers, hospitals, and clinics – in fact, the entire complex value network – needs to be made more coherent and collaborative across both the public and private sectors. And the bedrock of this coherence is data: quality shared and trusted data that is made available through effective and locally relevant technology platforms.
All human society is itself a vast network of people, families, neighbourhoods, cities, provinces, and countries, connected to each other by road, rail, air and sea links, by electricity grids, by telecommunications, and by water waste and reticulation grids. We are, at our core, a networked – and networking – social species.
Lately, the Fourth Industrial Revolution has enabled us to digitise these human networks by reducing each element within the network, as well as the interactions between different network elements, into data that can be collected, stored and analysed for insights that lead to value.
Examples of modern organisations doing this successfully abound, and many more have built networks that radically changes the economics, utility and convenience of life for billions of people around the world. The healthcare industry now faces the opportunity to exponentially improve its ability to secure positive patient outcomes if it can leverage its own networks. And the best way to do that – the only way, I would argue – is through technology.
Data insights gained from the healthcare network can also guide training and skills development to ensure healthcare professionals are equipped with the latest and most relevant medical theories and treatment information. This will hugely improve the delivery of patient care, especially in rural areas where access to information is often limited by poor connectivity or outdated technology.
Practical outcomes of bringing healthcare networks to life
When we combine the ability of networks to gather and connect data with tools such as machine learning to analyse data, we can start exploring ways networks can accelerate – or even automate – decision-making on behalf of network participants. This could have a transformative effect on the way healthcare professionals respond to outbreaks and epidemics by optimising the use of available resources, in real time, using actual on-the-ground data.
At an individual level, machine learning could also make cancer treatment more effective by identifying characteristics that help predict the effectiveness of a specific treatment for a specific patient. An oncologist could collate the results of all these tests, combine them with patient data and treatment results, and aggregate the data before machine learning algorithms are employed to discover which treatments are most effective. This way, you could reduce the amount of a treatment needed for a specific patient, as well as eliminate the occurrence of unnecessary dosages.
By pulling data from other sources within the healthcare network, such as data on localised genetic and environmental factors that could influence treatment effectiveness, healthcare providers suddenly increase the likelihood of ensuring a positive patient outcome exponentially. As sensor technologies become more commonly used, the data sets available to healthcare professionals will also increase exponentially.
However, without an appropriate technology platform that can collect, store, and analyse data in real time, healthcare providers will struggle to extract optimal value from the data sets.
Keep it local, relevant
In Africa, it is important to bear local technology limitations in mind. Even in the more developed markets such as South Africa, smartphone penetration is a lowly 40 percent. While this is likely to improve as the technology becomes more affordable, any technology-supported healthcare initiative must accommodate this lack of smartphone access.
Technology is not a solution in and of itself though. It is critical that the use of data in healthcare networks take cognisance of privacy and ethics over and above commercial interests. Care should also be taken of unintended consequences of technology implementation. For example, in the U.S., teen depression and suicide rates have increased dramatically since 2012, when smartphone penetration crossed the 50 percent mark.
As the digital transformation of the African continent picks up speed, new opportunities to improve the lives of all its citizens emerge. It is important that we start with a solid foundation – or network – that can integrate the various components of the healthcare value chain and deliver actionable insights and information to on-the-ground practitioners.